----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\cdj19\Desktop\Replication AJPS\Table 3 Estimates.log
  log type:  text
 opened on:  19 Jan 2016, 08:37:27

. do "C:\Users\cdj19\AppData\Local\Temp\STD01000000.tmp"

. ***** Data: Meritocracy Replication Data - Table 3 (for Table 3) *****
. 
. ** Model reported in paper: Logit model w/ random intercept & random slope **
. 
. xtmelogit havenot2 ginicnty income_i ginicntyXincome_i ///
>         income_cnty black_cnty perc_bush04 pop_cnty educ_i age_i gender_i unemp_i union_i partyid_i ideo_i attend_i ///
>         if white==1 || fips: income_i , cov(unstruct)

Refining starting values: 

Iteration 0:   log likelihood = -521.40043  
Iteration 1:   log likelihood = -514.11956  
Iteration 2:   log likelihood = -513.86873  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -513.86873  
Iteration 1:   log likelihood = -513.67609  
Iteration 2:   log likelihood = -513.64013  
Iteration 3:   log likelihood =  -513.6393  
Iteration 4:   log likelihood = -513.63929  

Mixed-effects logistic regression               Number of obs      =      1067
Group variable: fips                            Number of groups   =       661

                                                Obs per group: min =         1
                                                               avg =       1.6
                                                               max =        19

Integration points =   7                        Wald chi2(15)      =    101.48
Log likelihood = -513.63929                     Prob > chi2        =    0.0000

-----------------------------------------------------------------------------------
         havenot2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
         ginicnty |   2.432937   1.774455     1.37   0.170    -1.044931    5.910804
         income_i |  -1.707733   1.182514    -1.44   0.149    -4.025418    .6099515
ginicntyXincome_i |  -4.844641   2.944632    -1.65   0.100    -10.61601     .926732
      income_cnty |   .7498671   .7453547     1.01   0.314    -.7110012    2.210735
       black_cnty |  -.0201935   .7095695    -0.03   0.977    -1.410924    1.370537
      perc_bush04 |   2.151649   .7633468     2.82   0.005     .6555165    3.647781
         pop_cnty |   1.007161   .9229419     1.09   0.275    -.8017724    2.816094
           educ_i |  -.7275956   .3982687    -1.83   0.068    -1.508188    .0529968
            age_i |  -.0092435   .0064013    -1.44   0.149    -.0217898    .0033028
         gender_i |    .194962   .1889127     1.03   0.302       -.1753    .5652241
          unemp_i |  -.3824124   .2268677    -1.69   0.092    -.8270649    .0622401
          union_i |   .2889062   .2518322     1.15   0.251    -.2046759    .7824884
        partyid_i |  -1.602998     .31806    -5.04   0.000    -2.226385   -.9796121
           ideo_i |  -.5618069   .4504821    -1.25   0.212    -1.444736    .3211219
         attend_i |  -.0331046   .3010636    -0.11   0.912    -.6231784    .5569692
            _cons |   .3242738   .9898457     0.33   0.743    -1.615788    2.264336
-----------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
fips: Unstructured           |
                sd(income_i) |   1.388273   .9289886      .3740049    5.153143
                   sd(_cons) |   1.662207   .5548871      .8640371    3.197701
        corr(income_i,_cons) |         -1   .0000573            -1           1
------------------------------------------------------------------------------
LR test vs. logistic regression:     chi2(3) =     9.39   Prob > chi2 = 0.0245

Note: LR test is conservative and provided only for reference.

. 
. ** Alternative specifications **
. 
. * Linear probability model w/clustered ses *
. 
. reg havenot2 ginicnty income_i ginicntyXincome_i ///
>         income_cnty black_cnty perc_bush04 pop_cnty educ_i age_i gender_i unemp_i union_i partyid_i ideo_i attend_i ///
>         if white==1, cluster(fips)

Linear regression                                      Number of obs =    1067
                                                       F( 15,   660) =   15.34
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1743
                                                       Root MSE      =  .40508

                                      (Std. Err. adjusted for 661 clusters in fips)
-----------------------------------------------------------------------------------
                  |               Robust
         havenot2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
         ginicnty |   .2153697   .2656466     0.81   0.418    -.3062446    .7369839
         income_i |  -.4384262   .1475832    -2.97   0.003    -.7282154    -.148637
ginicntyXincome_i |  -.2903945   .3478972    -0.83   0.404    -.9735131    .3927241
      income_cnty |   .1001286   .1074405     0.93   0.352    -.1108378     .311095
       black_cnty |   .0047299   .0897715     0.05   0.958    -.1715423    .1810021
      perc_bush04 |   .2556946   .1014371     2.52   0.012     .0565164    .4548729
         pop_cnty |    .134499   .0598042     2.25   0.025     .0170695    .2519284
           educ_i |  -.1146331   .0590522    -1.94   0.053    -.2305858    .0013197
            age_i |  -.0012564   .0008718    -1.44   0.150    -.0029682    .0004554
         gender_i |   .0181678   .0246846     0.74   0.462     -.030302    .0666377
          unemp_i |  -.0490838   .0302463    -1.62   0.105    -.1084744    .0103069
          union_i |   .0318818   .0382419     0.83   0.405    -.0432087    .1069722
        partyid_i |  -.2197213     .04578    -4.80   0.000    -.3096132   -.1298294
           ideo_i |  -.0771074   .0650652    -1.19   0.236     -.204867    .0506523
         attend_i |  -.0210572   .0397063    -0.53   0.596    -.0990231    .0569087
            _cons |   .6230944    .146201     4.26   0.000     .3360194    .9101695
-----------------------------------------------------------------------------------

. 
. * Logit model w/clustered ses *
. 
. logit havenot2 ginicnty income_i ginicntyXincome_i ///
>         income_cnty black_cnty perc_bush04 pop_cnty educ_i age_i gender_i unemp_i union_i partyid_i ideo_i attend_i ///
>         if white==1, cluster(fips)

Iteration 0:   log pseudolikelihood = -619.24105  
Iteration 1:   log pseudolikelihood = -524.02289  
Iteration 2:   log pseudolikelihood = -518.37887  
Iteration 3:   log pseudolikelihood = -518.33449  
Iteration 4:   log pseudolikelihood = -518.33449  

Logistic regression                               Number of obs   =       1067
                                                  Wald chi2(15)   =     166.28
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -518.33449                 Pseudo R2       =     0.1630

                                      (Std. Err. adjusted for 661 clusters in fips)
-----------------------------------------------------------------------------------
                  |               Robust
         havenot2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
         ginicnty |   2.160823   1.455222     1.48   0.138    -.6913601    5.013006
         income_i |  -1.634084   1.023563    -1.60   0.110    -3.640231    .3720636
ginicntyXincome_i |  -4.362688   2.533341    -1.72   0.085    -9.327946    .6025694
      income_cnty |   .4695822    .693615     0.68   0.498    -.8898782    1.829043
       black_cnty |  -.0800179   .5918654    -0.14   0.892    -1.240053    1.080017
      perc_bush04 |   1.644241   .6325796     2.60   0.009     .4044074    2.884074
         pop_cnty |   1.012664   .4016925     2.52   0.012     .2253608    1.799966
           educ_i |  -.6014115   .3534914    -1.70   0.089    -1.294242    .0914189
            age_i |  -.0064511   .0051731    -1.25   0.212    -.0165902    .0036881
         gender_i |   .1279678   .1541537     0.83   0.406    -.1741678    .4301035
          unemp_i |  -.3238252   .1931949    -1.68   0.094    -.7024802    .0548298
          union_i |   .2522599   .2307328     1.09   0.274    -.1999681    .7044879
        partyid_i |  -1.355102   .2691528    -5.03   0.000    -1.882632    -.827572
           ideo_i |  -.4640851   .3706393    -1.25   0.211    -1.190525    .2623545
         attend_i |  -.0725703   .2405169    -0.30   0.763    -.5439747     .398834
            _cons |   .4148246   .8181618     0.51   0.612    -1.188743    2.018392
-----------------------------------------------------------------------------------

. 
. * Linear probability model w/ random intercept *
. 
. xtmixed havenot2 ginicnty income_i ginicntyXincome_i ///
>         income_cnty black_cnty perc_bush04 pop_cnty educ_i age_i gender_i unemp_i union_i partyid_i ideo_i attend_i ///
>         if white==1 || fips: ,

Performing EM optimization: 

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -542.05739  
Iteration 1:   log likelihood = -538.29613  
Iteration 2:   log likelihood = -538.29489  
Iteration 3:   log likelihood = -538.29489  

Computing standard errors:

Mixed-effects ML regression                     Number of obs      =      1067
Group variable: fips                            Number of groups   =       661

                                                Obs per group: min =         1
                                                               avg =       1.6
                                                               max =        19


                                                Wald chi2(15)      =    224.77
Log likelihood = -538.29489                     Prob > chi2        =    0.0000

-----------------------------------------------------------------------------------
         havenot2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
         ginicnty |   .2189443    .231553     0.95   0.344    -.2348913    .6727798
         income_i |  -.4221318   .1413962    -2.99   0.003    -.6992633   -.1450004
ginicntyXincome_i |  -.3275328   .3348399    -0.98   0.328    -.9838069    .3287413
      income_cnty |   .1127241   .1023564     1.10   0.271    -.0878906    .3133389
       black_cnty |  -.0017402   .0923993    -0.02   0.985    -.1828395    .1793591
      perc_bush04 |   .2777915   .0989886     2.81   0.005     .0837773    .4718056
         pop_cnty |   .1430629   .1403895     1.02   0.308    -.1320954    .4182212
           educ_i |  -.1144413   .0539353    -2.12   0.034    -.2201525   -.0087302
            age_i |  -.0012197   .0008627    -1.41   0.157    -.0029106    .0004712
         gender_i |   .0190584   .0250028     0.76   0.446    -.0299461     .068063
          unemp_i |  -.0539587   .0298935    -1.81   0.071    -.1125489    .0046315
          union_i |   .0311348   .0355157     0.88   0.381    -.0384746    .1007443
        partyid_i |  -.2179849   .0409432    -5.32   0.000    -.2982321   -.1377376
           ideo_i |   -.077534   .0615936    -1.26   0.208    -.1982552    .0431872
         attend_i |  -.0178569   .0407328    -0.44   0.661    -.0976917    .0619778
            _cons |   .6043455   .1340333     4.51   0.000     .3416451    .8670458
-----------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
fips: Identity               |
                   sd(_cons) |   .1356233   .0300784      .0878123    .2094657
-----------------------------+------------------------------------------------
                sd(Residual) |   .3791171    .012254      .3558447    .4039115
------------------------------------------------------------------------------
LR test vs. linear regression: chibar2(01) =     6.87 Prob >= chibar2 = 0.0044

. 
. * Logit model w/ random intercept *
. 
. xtmelogit havenot2 ginicnty income_i ginicntyXincome_i ///
>         income_cnty black_cnty perc_bush04 pop_cnty educ_i age_i gender_i unemp_i union_i partyid_i ideo_i attend_i ///
>         if white==1 || fips: ,

Refining starting values: 

Iteration 0:   log likelihood = -517.84038  
Iteration 1:   log likelihood = -515.00447  
Iteration 2:   log likelihood = -514.96158  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -514.96158  
Iteration 1:   log likelihood = -514.96154  

Mixed-effects logistic regression               Number of obs      =      1067
Group variable: fips                            Number of groups   =       661

                                                Obs per group: min =         1
                                                               avg =       1.6
                                                               max =        19

Integration points =   7                        Wald chi2(15)      =    100.17
Log likelihood = -514.96154                     Prob > chi2        =    0.0000

-----------------------------------------------------------------------------------
         havenot2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
         ginicnty |   2.273193   1.588786     1.43   0.152    -.8407707    5.387156
         income_i |  -1.951126   1.109801    -1.76   0.079    -4.126296    .2240437
ginicntyXincome_i |  -4.712627   2.731452    -1.73   0.084    -10.06617    .6409209
      income_cnty |   .6713788   .7443221     0.90   0.367    -.7874658    2.130223
       black_cnty |   -.100348   .6806845    -0.15   0.883    -1.434465    1.233769
      perc_bush04 |   2.098435    .743045     2.82   0.005     .6420941    3.554777
         pop_cnty |   1.256324   1.008484     1.25   0.213    -.7202681    3.232916
           educ_i |  -.7115797   .3853068    -1.85   0.065    -1.466767    .0436077
            age_i |  -.0077823    .006056    -1.29   0.199    -.0196518    .0040873
         gender_i |     .14792    .180131     0.82   0.412    -.2051302    .5009702
          unemp_i |  -.3767351   .2176701    -1.73   0.083    -.8033606    .0498905
          union_i |   .2985829   .2517469     1.19   0.236    -.1948319    .7919977
        partyid_i |   -1.52325   .3006962    -5.07   0.000    -2.112604    -.933896
           ideo_i |  -.5780174   .4289591    -1.35   0.178    -1.418762     .262727
         attend_i |  -.0581354   .2892282    -0.20   0.841    -.6250122    .5087414
            _cons |   .4070692   .9276001     0.44   0.661    -1.410994    2.225132
-----------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
fips: Identity               |
                   sd(_cons) |    .943306    .261787      .5475534    1.625095
------------------------------------------------------------------------------
LR test vs. logistic regression: chibar2(01) =     6.75 Prob>=chibar2 = 0.0047

. 
. * Linear probability model w/ random intercept & random slope *
. 
. xtmixed havenot2 ginicnty income_i ginicntyXincome_i ///
>         income_cnty black_cnty perc_bush04 pop_cnty educ_i age_i gender_i unemp_i union_i partyid_i ideo_i attend_i ///
>         if white==1 || fips: income_i , cov(unstruct)

Performing EM optimization: 

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -534.39109  
Iteration 1:   log likelihood = -528.87846  
Iteration 2:   log likelihood = -516.21043  
Iteration 3:   log likelihood = -515.09943  
Iteration 4:   log likelihood = -515.08683  
Iteration 5:   log likelihood = -515.08683  

Computing standard errors:

Mixed-effects ML regression                     Number of obs      =      1067
Group variable: fips                            Number of groups   =       661

                                                Obs per group: min =         1
                                                               avg =       1.6
                                                               max =        19


                                                Wald chi2(15)      =    203.80
Log likelihood = -515.08683                     Prob > chi2        =    0.0000

-----------------------------------------------------------------------------------
         havenot2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
         ginicnty |   .1238102   .2598542     0.48   0.634    -.3854946    .6331151
         income_i |  -.4663299   .1453943    -3.21   0.001    -.7512975   -.1813622
ginicntyXincome_i |  -.1818383   .3483923    -0.52   0.602    -.8646746    .5009979
      income_cnty |   .1244675   .0948109     1.31   0.189    -.0613585    .3102935
       black_cnty |   .0308811   .0899104     0.34   0.731      -.14534    .2071022
      perc_bush04 |   .2662663   .0964351     2.76   0.006      .077257    .4552757
         pop_cnty |   .0454927   .1055653     0.43   0.667    -.1614115    .2523968
           educ_i |  -.1129465   .0521394    -2.17   0.030    -.2151378   -.0107552
            age_i |  -.0012863   .0008448    -1.52   0.128    -.0029421    .0003695
         gender_i |   .0305988   .0239264     1.28   0.201    -.0162961    .0774938
          unemp_i |  -.0520534   .0290392    -1.79   0.073    -.1089693    .0048624
          union_i |   .0321508   .0334625     0.96   0.337    -.0334344     .097736
        partyid_i |  -.2124958   .0396695    -5.36   0.000    -.2902467    -.134745
           ideo_i |  -.0679577   .0606286    -1.12   0.262    -.1867875    .0508721
         attend_i |   .0012403   .0392679     0.03   0.975    -.0757234    .0782039
            _cons |   .6119732   .1384315     4.42   0.000     .3406524     .883294
-----------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
fips: Unstructured           |
                sd(income_i) |   .4285015   .0602096       .325348    .5643602
                   sd(_cons) |    .409291   .0396672      .3384827    .4949119
        corr(income_i,_cons) |         -1   2.97e-06            -1           1
-----------------------------+------------------------------------------------
                sd(Residual) |   .3437833   .0104763      .3238512    .3649421
------------------------------------------------------------------------------
LR test vs. linear regression:       chi2(3) =    53.29   Prob > chi2 = 0.0000

Note: LR test is conservative and provided only for reference.

. 
end of do-file

. log close
      name:  <unnamed>
       log:  C:\Users\cdj19\Desktop\Replication AJPS\Table 3 Estimates.log
  log type:  text
 closed on:  19 Jan 2016, 08:45:19
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
